DatabricksSubmitRunOperator¶

Use the DatabricksSubmitRunOperator to submit a new Databricks job via Databricks api/2.1/jobs/runs/submit API endpoint.

Using the Operator¶

There are two ways to instantiate this operator. In the first way, you can take the JSON payload that you typically use to call the api/2.1/jobs/runs/submit endpoint and pass it directly to our DatabricksSubmitRunOperator through the json parameter.

Another way to accomplish the same thing is to use the named parameters of the DatabricksSubmitRunOperator directly. Note that there is exactly one named parameter for each top level parameter in the runs/submit endpoint.

Parameter	Input
spark_jar_task: dict	main class and parameters for the JAR task
notebook_task: dict	notebook path and parameters for the task
spark_python_task: dict	python file path and parameters to run the python file with
spark_submit_task: dict	parameters needed to run a spark-submit command
pipeline_task: dict	parameters needed to run a Delta Live Tables pipeline
new_cluster: dict	specs for a new cluster on which this task will be run
existing_cluster_id: string	ID for existing cluster on which to run this task
libraries: list of dict	libraries which this run will use
run_name: string	run name used for this task
timeout_seconds: integer	The timeout for this run
databricks_conn_id: string	the name of the Airflow connection to use
polling_period_seconds: integer	controls the rate which we poll for the result of this run
databricks_retry_limit: integer	amount of times retry if the Databricks backend is unreachable
databricks_retry_delay: decimal	number of seconds to wait between retries
databricks_retry_args: dict	An optional dictionary with arguments passed to `tenacity.Retrying` class.
do_xcom_push: boolean	whether we should push run_id and run_page_url to xcom

Examples¶

Specifying parameters as JSON¶

An example usage of the DatabricksSubmitRunOperator is as follows:

airflow/providers/databricks/example_dags/example_databricks.py[source]

    # Example of using the JSON parameter to initialize the operator.
    new_cluster = {
        'spark_version': '9.1.x-scala2.12',
        'node_type_id': 'r3.xlarge',
        'aws_attributes': {'availability': 'ON_DEMAND'},
        'num_workers': 8,
    }

    notebook_task_params = {
        'new_cluster': new_cluster,
        'notebook_task': {
            'notebook_path': '/Users/airflow@example.com/PrepareData',
        },
    }

    notebook_task = DatabricksSubmitRunOperator(task_id='notebook_task', json=notebook_task_params)

Using named parameters¶

You can also use named parameters to initialize the operator and run the job.

airflow/providers/databricks/example_dags/example_databricks.py[source]

    # Example of using the named parameters of DatabricksSubmitRunOperator
    # to initialize the operator.
    spark_jar_task = DatabricksSubmitRunOperator(
        task_id='spark_jar_task',
        new_cluster=new_cluster,
        spark_jar_task={'main_class_name': 'com.example.ProcessData'},
        libraries=[{'jar': 'dbfs:/lib/etl-0.1.jar'}],
    )